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Integration of production planning and scheduling using an expert system and a genetic algorithm

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  • A Ławrynowicz

    (University of Technology and Life Sciences)

Abstract

In the traditional approaches, processes of planning and scheduling are done sequentially, where the process plan is determined before the actual scheduling is performed. This simple approach ignores the relationship between the scheduling and planning. Practical scheduling systems need to be able to react to significant real-time events within an acceptable response time and revise schedules appropriately. Therefore, the author proposes a new methodology with artificial intelligence to support production planning and scheduling in supply net. In this approach, the production planning problem is first solved, and then the scheduling problem is considered with the constraint of the solution. The approach is implemented as a combination of expert system and genetic algorithm. The research indicates that the new system yields better results in real-life supply net than using a traditional method. The results of experiments provide that the proposed genetic algorithm produces schedules with makespan that is average 21% better than the methods based on dispatching rules.

Suggested Citation

  • A Ławrynowicz, 2008. "Integration of production planning and scheduling using an expert system and a genetic algorithm," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(4), pages 455-463, April.
    • Handle: RePEc:pal:jorsoc:v:59:y:2008:i:4:d:10.1057_palgrave.jors.2602423
    DOI: 10.1057/palgrave.jors.2602423
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    References listed on IDEAS

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    Cited by:

    1. S. Zhang & T. N. Wong, 2018. "Integrated process planning and scheduling: an enhanced ant colony optimization heuristic with parameter tuning," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 585-601, March.
    2. Toorajipour, Reza & Sohrabpour, Vahid & Nazarpour, Ali & Oghazi, Pejvak & Fischl, Maria, 2021. "Artificial intelligence in supply chain management: A systematic literature review," Journal of Business Research, Elsevier, vol. 122(C), pages 502-517.
    3. Sicheng Zhang & T.N. Wong, 2016. "Studying the impact of sequence-dependent set-up times in integrated process planning and scheduling with E-ACO heuristic," International Journal of Production Research, Taylor & Francis Journals, vol. 54(16), pages 4815-4838, August.
    4. Ławrynowicz Anna, 2011. "Genetic Algorithms for Solving Scheduling Problems in Manufacturing Systems," Foundations of Management, Sciendo, vol. 3(2), pages 7-26, January.

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